AUGMENTED AI-KNOWLEDGE DRIVEN INTELLIGENT SYSTEMS FOR ADVERSARIAL-DYNAMIC UNCERTAINTY AND COMPLEXITY

Abstract

Goal: ISO 31000 Risk Management (RM) recently re-defined risk as the effect of uncertainty on an organization's ability to meet the objectives. Earlier, it defined risk as a combination of the probability and scope of the (predicted) consequences. The revised ISO Risk advances beyond a static world guided by prediction and pre-determination based on historical data to a dynamic world characterized by uncertainty and complexity focused on business outcomes over data inputs. Our Knowledge Management (KM) R&D adopted by global organizations such as Nasa and Big Banks is readily applicable to provide a 25-year head start to organizations in above ISO risk evolution. Results: Over the last two decades, we have developed theoretical and applied frameworks for the dynamic world characterized by uncertainty and complexity, with business outcomes as drivers of real-time performance rather than data inputs. Our forward-looking anticipation of surprise focus of KM drives future organizational adaptation, survival and competence in face of discontinuous environmental change at organizations such as Goldman Sachs. Our KM focus manages change, uncertainty and complexity as primary (outcome) targets in contrast to data-driven (input) approaches. Its focus on dynamic uncertainty is complemented by adversarial uncertainty from cyber-adversarial environments. Originality | Value: Quantum uncertainty – encapsulating the two uncertainty types – and time-space complexity from increasingly non-deterministic and statistically non-normal and non-linear environments are the focus of our KM R&D underpinning development of quantum minds. Our latest AI-Cybersecurity KM practices are advancing the future of Pentagon’s C4I-Cyber-Command-Control-Advanced Battle Management Systems and AWS Network-Centric Agile-Resilient Cloud Computing.